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Glama

Nhtsa

by io.github.pipeworx-io

Remember

remember
Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Adds rich context beyond annotations: scoping by identifier, persistent memory for authenticated users vs 24-hour retention for anonymous sessions, and the key-value storage model. Annotations only indicate idempotent and non-destructive, so the description fills important behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: purpose, when-to-use, and storage/retention behavior. Each sentence earns its place, the main action is front-loaded, and there is no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter tool with no output schema, the description covers all essential aspects: purpose, usage triggers, persistence semantics, and relationship with sibling tools. It is fully sufficient for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with already descriptive parameter explanations (key examples, value as any text). The description adds the key-value pair context but does not substantially extend understanding beyond what the schema provides, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a clear verb ('Save') and defines the resource as data for later reuse, with concrete examples (ticker, address, preference). It also distinguishes this from sibling tools recall and forget by explaining the memory lifecycle.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use ('Use when you discover something worth carrying forward') and provides alternatives by pairing with recall for retrieval and forget for deletion, making the selection context clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Nhtsa toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

The descriptions are extraordinarily detailed and do a lot of work to differentiate tools, but there is real functional overlap: three ask_pipeworx variants, six Polymarket/bet tools (bet_research, polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) that all target identifying betting/value opportunities, and overlapping ai_visibility_check vs scan_competitor_ai_presence. A capable agent could navigate it, but misselection risk is high.

Naming Consistency3/5

Mostly snake_case and readable, but the verb/noun placement is inconsistent: verb-first (get_makes, list_subscriptions, resolve_entity, decode_vin) mixes with noun-first (entity_profile, bet_research, pipeworx_trending) and branded prefixes (ask_pipeworx, pipeworx_feedback, polymarket_*). No chaotic camelCase mixing, but no single predictable pattern either.

Tool Count2/5

37 tools is well beyond the heavy threshold, and the server named 'Nhtsa' carries only ~6 vehicle-specific tools while the rest is a general-purpose research platform spanning prediction markets, memory, npm packages, AI-marketing audits, and subscriptions. The scope is overloaded and the name badly misrepresents the content, making the surface feel sprawling rather than focused.

Completeness4/5

For the NHTSA vehicle domain it covers the lookup surface well (makes, models, recalls, complaints, safety ratings, VIN decode), and the broader research platform is genuinely deep with grounding, grounding-with-evidence, discovery, subscription, and memory support. Minor gaps exist (no direct vehicle-make year filtering beyond three fields, USPTO patent APIs are soft-failing), but no dead ends for core workflows.

Resources